4 research outputs found
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Charged domain walls in improper ferroelectric hexagonal manganites and gallates
Ferroelectric domain walls are attracting broad attention as atomic-scale switches, diodes, and mobile wires for next-generation nanoelectronics. Charged domain walls in improper ferroelectrics are particularly interesting as they offer multifunctional properties and an inherent stability not found in proper ferroelectrics. Here we study the energetics and structure of charged walls in improper ferroelectric YMnO3,InMnO3, and YGaO3 by first-principles calculations and phenomenological modeling. Positively and negatively charged walls are asymmetric in terms of local structure and width. The wall width scales with the amplitude of the primary structural order parameter and the coupling strength to the polarization, reflecting that polarization is not the driving force for domain formation. We introduce general rules for how to engineer n- and p-type domain wall conductivity based on the domain size, polarization, and electronic band gap. This opens the possibility of fine tuning the local transport properties and designing p-n-junctions for domain wall-based nanocircuitry
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Application of a long short-term memory for deconvoluting conductance contributions at charged ferroelectric domain walls
Ferroelectric domain walls are promising quasi-2D structures that can be leveraged for miniaturization of electronics components and new mechanisms to control electronic signals at the nanoscale. Despite the significant progress in experiment and theory, however, most investigations on ferroelectric domain walls are still on a fundamental level, and reliable characterization of emergent transport phenomena remains a challenging task. Here, we apply a neural-network-based approach to regularize local I(V)-spectroscopy measurements and improve the information extraction, using data recorded at charged domain walls in hexagonal (Er0.99,Zr0.01)MnO3 as an instructive example. Using a sparse long short-term memory autoencoder, we disentangle competing conductivity signals both spatially and as a function of voltage, facilitating a less biased, unconstrained and more accurate analysis compared to a standard evaluation of conductance maps. The neural-network-based analysis allows us to isolate extrinsic signals that relate to the tip-sample contact and separating them from the intrinsic transport behavior associated with the ferroelectric domain walls in (Er0.99,Zr0.01)MnO3. Our work expands machine-learning-assisted scanning probe microscopy studies into the realm of local conductance measurements, improving the extraction of physical conduction mechanisms and separation of interfering current signals